Predict with Custom Context

What exists today

sdm-client builds one Request type for every model, and the direct builder — Request(context=..., predict=..., target=..., task=...) — accepts related_tables= in its signature. A relational request is gathered, not assembled from frames, though: the neighborhood around each entity is sampled and folded onto the instance table, which is what materialize() does. So a directly-built request is refused when you name model=KumoRelational(), even with related_tables=:

from sdm_client import Request, SDMClient, KumoRelational
request = Request(
    context=labelled,
    predict=to_score,
    target="churn",
    task="classification",
    related_tables={"orders": orders_df},
)
with SDMClient(url="http://localhost:8000") as client:
    client.predict(request, model=KumoRelational())
# UnsupportedRequestError: a relational request has to be gathered first...

The direct builder works for Kumo Tabular, where it is the primary path — see Kumo Tabular: Make Predictions.

What to use instead

Build the request from PQL, as described in Make Predictions and Write Predictive Queries:

from sdm_client import PredictiveQuery
query = PredictiveQuery(query_text, schema)
request = query.materialize(indices=entity_ids)

materialize() derives context examples from the schema’s own historical data; there is currently no supported way to substitute context you constructed yourself for the relational model.

If your workflow specifically needs hand-selected relational context — for example, to test how a particular set of examples affects prediction quality — that capability is not available in this client version. Check the changelog for updates.